Camtasia在基于操作的网络视频教程录播中的应用

通过Camtasia Recorder录制屏幕操作视频教程,应用Camtasia Studio编辑视频、声音、图片等媒体剪辑元素,为视频添加标题、转场、配音、变焦、字幕等效果,提高视频的质量,按照用户需要的格式输出视频教程。     

Real time repeated video sequence identification

In this paper, we present a real time system for detecting repeated video clips from a live video source such as news broadcasts. Our system utilizes customized temporal video segmentation techniques to automatically partition the digital video signal into semantically sensible shots and scenes. As each frame of the video source is processed, we extract auxiliary information to facilitate repeated sequence detection. When the video transition marking the end of the shot/scene is detected, we are able to rapidly locate all previous occurrences of the video clip. Our objective is to use repeated sequence information in our multimedia content analysis application to deduce semantic relationships among shots/scenes in the input video. Our real time video processing techniques are independent of source and domain and can be applied to other applications such as commercial detection and improved video compression.     

一种通过视频片段进行视频检索的方法

视频片段检索是基于内容的视频检索的主要方式,它需要解决两个问题:(1) 从视频库里自动分割出与查询片段相似的多个片段;(2) 按照相似度从高到低排列这些相似片段.首次尝试运用图论的匹配理论来解决这两个问题.针对问题(1),把检索过程分为两个阶段:镜头检索和片段检索.在镜头检索阶段,利用相机运动信息,一个变化较大的镜头被划分为几个内容一致的子镜头,两个镜头的相似性通过对应子镜头的相似性计算得到;在片段检索阶段,通过考察相似镜头的连续性初步得到一个个相似片段,再运用最大匹配的Hungarian算法来确定真正的相似片段.针对问题(2),考虑了片段相似性判断的视觉、粒度、顺序和干扰因子,提出用最优匹配的Kuhn-Munkres算法和动态规划算法相结合,来解决片段相似度的度量问题.实验对比结果表明,所提出的方法在片段检索中可以取得更高的检索精度和更快的检索速度.     

利用等价关系理论进行视频片段检索的方法

视频片段检索是基于内容的视频检索的主要方式,可是现有的片段检索方法大多只是对预先分割好的片段进行检索。为了从连续的视频节目中自动分割出多个相似的片段,提出了一种新的有效的视频片段检索方法,并首次尝试将等价关系理论应用于视频片段的检索.该方法首先用等价关系理论定义了片段匹配函数,同时采用滑动镜头窗自动分割出多个真正相似的片段;然后把等价类映射为矩阵表达形式,再通过矩阵的特性来度量影响片段相似度的不同因子,实现了相似片段的排序。实验结果表明,该方法能够一次性快速准确地从连续视频库中自动分割出与查询片段相似的多个片段。     

Abnormal visual event detection based on multi-instance learning and autoregressive integrated moving average model in edge-based Smart City surveillance

The abnormal visual event detection is an important subject in Smart City surveillance where a lot of data can be processed locally in edge computing environment. Real-time and detection effectiveness are critical in such an edge environment. In this paper, we propose an abnormal event detection approach based on multi-instance learning and autoregressive integrated moving average model for video surveillance of crowded scenes in urban public places, focusing on real-time and detection effectiveness. We propose an unsupervised method for abnormal event detection by combining multi-instance visual feature selection and the autoregressive integrated moving average model. In the proposed method, each video clip is modeled as a visual feature bag containing several subvideo clips, each of which is regarded as an instance. The time-transform characteristics of the optical flow characteristics within each subvideo clip are considered as a visual feature instance, and time-series modeling is carried out for multiple visual feature instances related to all subvideo clips in a surveillance video clip. The abnormal events in each surveillance video clip are detected using the multi-instance fusion method. This approach is verified on publically available urban surveillance video datasets and compared with state-of-the-art alternatives. Experimental results demonstrate that the proposed method has better abnormal event detection performance for crowded scene of urban public places with an edge environment.     

一种基于视频的人体动画骨架提取技术

人体动画是计算机动画中最具有挑战性的领域．针对传统人体动画技术的不足,提出了一种新技术来提取视频中的人体骨架,以应用于人体动画．对于任何视频流,该方法都能在图像序列中跟踪人体特征骨架,并建立了透视投影下的三维人体运动骨架序列,最终通过自动注释运动信息建立了可供动画师浏览、查询的运动信息库．这种方法具有素材来源丰富、计算量少、制作高效等特点,而且产生了人体运动非常真实,同时也将动画师从枯燥的工作中解     

A spatio-temporal pyramid matching for video retrieval

An efficient video retrieval system is essential to search relevant video contents from a large set of video clips, which typically contain several heterogeneous video clips to match with. In this paper, we introduce a content-based video matching system that finds the most relevant video segments from video database for a given query video clip. Finding relevant video clips is not a trivial task, because objects in a video clip can constantly move over time. To perform this task efficiently, we propose a novel video matching called Spatio-Temporal Pyramid Matching (STPM). Considering features of objects in 2D space and time, STPM recursively divides a video clip into a 3D spatio-temporal pyramidal space and compares the features in different resolutions. In order to improve the retrieval performance, we consider both static and dynamic features of objects. We also provide a sufficient condition in which the matching can get the additional benefit from temporal information. The experimental results show that our STPM performs better than the other video matching methods.     

Remix Literacy and Fan Compositions

Although remixing has lately received increased scholarly attention in the rhetoric and composition community, studies commonly focus on examples of remixed objects rather than the compositional strategies used by remix composers themselves. In this study, I recount the voices of individuals who participate in online communities where videos, music, and texts from popular culture are remixed by fans: Lost Video Island, OverClocked ReMix, and Remix Redux. The aims and abilities these composers described to me constitute a developing area of digital literacy that I call remix literacy, a term that draws attention to the skills needed to create remixes that are deemed effective by communities. I find that the skills of a remix literate composer are fundamentally rhetorical, making this area of study important both for literacy scholars interested in understanding the self-sponsored activities of creative composers online and for composition instructors concerned with adapting their pedagogies to the skills I have found are needed for effective communication in fan communities.     

Gaussian Mixture Modeling of Short-Time Fourier Transform Features for Audio Fingerprinting

In audio fingerprinting, an audio clip must be recognized by matching an extracted fingerprint to a database of previously computed fingerprints. The fingerprints should reduce the dimensionality of the input significantly, provide discrimination among different audio clips, and, at the same time, be invariant to distorted versions of the same audio clip. In this paper, we design fingerprints addressing the above issues by modeling an audio clip by Gaussian mixture models (GMM). We evaluate the performance of many easy-to-compute short-time Fourier transform features, such as Shannon entropy, Renyi entropy, spectral centroid, spectral bandwidth, spectral flatness measure, spectral crest factor, and Mel-frequency cepstral coefficients in modeling audio clips using GMM for fingerprinting. We test the robustness of the fingerprints under a large number of distortions. To make the system robust, we use some of the distorted versions of the audio for training. However, we show that the audio fingerprints modeled using GMM are not only robust to the distortions used in training but also to distortions not used in training. Among the features tested, spectral centroid performs best with an identification rate of 99.2% at a false positive rate of 10^-4. All of the features give an identification rate of more than 90% at a false positive rate of 10^-3     

基于拓扑独立成分分析和高斯混合模型的视频语义概念检测

针对目前词袋模型(BoW)视频语义概念检测方法中的量化误差问题,为了更有效地自动提取视频的底层特征,提出一种基于拓扑独立成分分析(TICA)和高斯混合模型(GMM)的视频语义概念检测算法。首先,通过TICA算法进行视频片段的特征提取,该特征提取算法能够学习到视频片段复杂不变性特征;其次利用GMM方法对视频视觉特征进行建模,描述视频特征的分布情况;最后构造视频片段的GMM超向量,采用支持向量机(SVM)进行视频语义概念检测。GMM是BoW概率框架下的拓展,能够减少量化误差,具有良好的鲁棒性。在TRECVID 2012和OV两个视频库上,将所提方法与传统的BoW、SIFT-GMM方法进行了对比实验,结果表明,基于TICA和GMM的视频语义概念检测方法能够提高视频语义概念检测的准确率。     

基于目标变化的监控视频关键帧提取方法

监控视频关键帧提取技术作为监控视频分析的重要研究内容,能够有效地解决视频数据的高效存储和快速访问等问题。本文提出一种基于目标变化的监控视频关键帧提取方法,分析监控视频帧间的目标变化,并采用局部极大值优化方法将原监控视频划分成视频片段。最后,从每个视频片段中选取特征中心对应视频帧作为关键帧,并依据目标的属性删除冗余的关键帧得到最终的视频关键帧集合。实验结果表明,该方法所提取的视频关键帧冗余性较低,所包含的内容很具有代表性。同时,该方法的复杂度较低,适用于监控视频的关键帧提取工作。     

相似动画视频片段探测方法

为了实现相似视频片段的快速探测,以动画视频片段为研究对象,提出一种建立在视频单元层上的动画视频片段探测方法.在视频特征描述阶段,采用更符合动画图像的Markov平稳特征来描述动画视频帧的视觉特征,并利用视频距离轨迹(VDT)来挖掘视频片段特征,同时采用线性拟合特征的描述方法来描述VDT的特征;在特征匹配阶段,将视频片段匹配问题转换为网络流优化的问题,通过将视频单元的时间一致性嵌入到匹配网络中来寻找最佳对齐方式,大幅度减少了匹配的数据量.实验结果表明,该方法极大地改善了相似视频片段的探测效果,与传统的视频匹配方法相比,其具有更好的鲁棒性以及更高的效率.     

Pattern classification models for classifying and indexing audio signals

In the age of digital information, audio data has become an important part in many modern computer applications. Audio classification and indexing has been becoming a focus in the research of audio processing and pattern recognition. In this paper, we propose effective algorithms to automatically classify audio clips into one of six classes: music, news, sports, advertisement, cartoon and movie. For these categories a number of acoustic features that include linear predictive coefficients, linear predictive cepstral coefficients and mel-frequency cepstral coefficients are extracted to characterize the audio content. The autoassociative neural network model (AANN) is used to capture the distribution of the acoustic feature vectors. Then the proposed method uses a Gaussian mixture model (GMM)-based classifier where the feature vectors from each class were used to train the GMM models for those classes. During testing, the likelihood of a test sample belonging to each model is computed and the sample is assigned to the class whose model produces the highest likelihood. Audio clip extraction, feature extraction, creation of index, and retrieval of the query clip are the major issues in automatic audio indexing and retrieval. A method for indexing the classified audio using LPCC features and k-means clustering algorithm is proposed.     

Analysis of Minimum Distances in High-Dimensional Musical Spaces

We propose an automatic method for measuring content-based music similarity, enhancing the current generation of music search engines and recommended systems. Many previous approaches to track similarity require brute-force, pair-wise processing between all audio features in a database and therefore are not practical for large collections. However, in an Internet-connected world, where users have access to millions of musical tracks, efficiency is crucial. Our approach uses features extracted from unlabeled audio data and near-neigbor retrieval using a distance threshold, determined by analysis, to solve a range of retrieval tasks. The tasks require temporal features-analogous to the technique of shingling used for text retrieval. To measure similarity, we count pairs of audio shingles, between a query and target track, that are below a distance threshold. The distribution of between-shingle distances is different for each database; therefore, we present an analysis of the distribution of minimum distances between shingles and a method for estimating a distance threshold for optimal retrieval performance. The method is compatible with locality-sensitive hashing (LSH)-allowing implementation with retrieval times several orders of magnitude faster than those using exhaustive distance computations. We evaluate the performance of our proposed method on three contrasting music similarity tasks: retrieval of mis-attributed recordings (fingerprint), retrieval of the same work performed by different artists (cover songs), and retrieval of edited and sampled versions of a query track by remix artists (remixes). Our method achieves near-perfect performance in the first two tasks and 75% precision at 70% recall in the third task. Each task was performed on a test database comprising 4.5 million audio shingles.     

基于关键帧序列的视频片段检索

提出了一种基于关键帧融合的视频片段检索方法。使用特征联合分布直方图将视频分割为子镜头,子镜头用关键帧表示。检索时,对范例视频片段的每个关键帧检索到相似的关键帧,所有的相似关键帧按照时间连续性融合为视频片段。提出一种快速的视频片段相似度计算模型。实验表明,本文的方法快速有效。     

基于模糊直方图的两阶段相似视频的自动鉴别和检索

针对不同视频拷贝的相似性自动鉴别问题背景,给出了一个兼顾效率及效果的由粗到精的两阶段方案.首先抽取视频帧内容的模糊颜色直方图特征向量,构成特征空间中反映该视频性质的特征点集合和相应轨迹线,而后从特征点空间分布的角度计算位置分布直方图向量,在此基础上实现初步快速过滤,最后用简化时序特征轨迹线匹配实现更精确的相似视频鉴别.     

基于音频分析的视频场景检测

目前场景检测的研究,主要是基于图像和视频。但音频同样具有丰富的场景信息,基于音频分析的计算量是比较少的,对自动或者半自动的场景检测,基于音频分析的方法也是更为让用户接受的。可以把基于音频分析的方法作为视频场景检测的辅助手段,以获得更为准确的场景检测和分割。该文提出了一个基于内容的音频分析系统,对视频序列实现基于音频分析的场景检测和分割。该系统能有效的解决许多诸如图像变化了,而实际场景并未变化的情形。且本系统整体运算复杂度较基于视频／图像的场景检测与分割系统要低。     

Bayesian belief network based broadcast sports video indexing

This paper presents a probabilistic Bayesian belief network (BBN) method for automatic indexing of excitement clips of sports video sequences. The excitement clips from sports video sequences are extracted using audio features. The excitement clips are comprised of multiple subclips corresponding to the events such as replay, field-view, close-ups of players, close-ups of referees/umpires, spectators, players’ gathering. The events are detected and classified using a hierarchical classification scheme. The BBN based on observed events is used to assign semantic concept-labels to the excitement clips, such as goals, saves, and card in soccer video, wicket and hit in cricket video sequences. The BBN based indexing results are compared with our previously proposed event-association based approach and found BBN is better than the event-association based approach. The proposed scheme provides a generalizable method for linking low-level video features with high-level semantic concepts. The generic nature of the proposed approach in the sports domain is validated by demonstrating successful indexing of soccer and cricket video excitement clips. The proposed scheme offers a general approach to the automatic tagging of large scale multimedia content with rich semantics. The collection of labeled excitement clips provide a video summary for highlight browsing, video skimming, indexing and retrieval.